Lecture_5-DataVis.R

setwd("~/Desktop/All_Things_School/UCSD/Courses/BGGN213/BGGN213/bimm143_05_rstats")

# chart of baby weight to age
babies <- read.table("weight_chart.txt", header=T, sep="\t")
plot(x=babies$Age, y=babies$Weight, type="o", pch=15)

# counts of genomic features
f_c <- read.table("feature_counts.txt", header=T, sep="\t")

old.par <- par()$mar
par(mar=c(3.1, 11.1, 4.1, 2.1))
barplot(f_c$Count, names.arg=f_c$Feature, horiz=T, las=1)

dotchart(f_c$Count, labels=f_c$Feature)

# male and female counts with rainbow colors in plot

mf_counts <- read.table("male_female_counts.txt", header=T, sep="\t")

par(mar=c(7.1, 4.1, 4.1, 2.1))
barplot(mf_counts$Count, names.arg=mf_counts$Sample, col=rainbow(nrow(mf_counts)), las=2)

par(mar=old.par)

# gene expression profile
u_d_expr <- read.table("up_down_expression.txt", header=T, sep="\t")
head(u_d_expr)

##         Gene Condition1 Condition2      State
## 1      A4GNT -3.6808610 -3.4401355 unchanging
## 2       AAAS  4.5479580  4.3864126 unchanging
## 3      AASDH  3.7190695  3.4787276 unchanging
## 4       AATF  5.0784720  5.0151916 unchanging
## 5       AATK  0.4711421  0.5598642 unchanging
## 6 AB015752.4 -3.6808610 -3.5921390 unchanging

nrow(u_d_expr)

## [1] 5196

table(u_d_expr$State)

## 
##       down unchanging         up 
##         72       4997        127

par(mar=c(5.1, 5.1, 4.1, 2.1))
plot(u_d_expr$Condition1, u_d_expr$Condition2, col=u_d_expr$State)

palette(c("blue", "grey", "red"))

# methylation data
meth <- read.table("expression_methylation.txt", header=T, sep="\t")
head(meth)

##          X expression promoter.meth gene.meth
## 1    Aars2   3.752997     3.5555556  85.31092
## 2   Aarsd1   5.335752    10.9027780  35.25517
## 3    Aasdh   5.605033     0.9935898  89.65585
## 4 Aasdhppt   3.842794     5.5024514  40.66456
## 5     Aass   4.484659     0.0000000  78.51562
## 6     Aatf   6.163478     0.4662005  81.74055

nrow(meth)

## [1] 9241

plot(meth$gene.meth, meth$expression)

dcols <- densCols(meth$gene.meth, meth$expression)
plot(meth$gene.meth, meth$expression, col=dcols)

inds <- meth$expression > 0
dcols <- densCols(meth$gene.meth[inds], meth$expression[inds])
plot(meth$gene.meth[inds], meth$expression[inds], col=dcols, pch=20)

c_r <- colorRampPalette(c("blue", "green", "red", "yellow"))
dcols <- densCols(meth$gene.meth[inds], meth$expression[inds], 
                  colramp = colorRampPalette(c("blue", "green", "red", "yellow"))
                  )
plot(meth$gene.meth[inds], meth$expression[inds], col=dcols, pch=20)

smoothScatter(meth$gene.meth[inds], meth$expression[inds])